docstr: Understanding Architectures in MLP Regressor
  https://stackoverflow.com/questions/35363530/python-scikit-learn-mlpclassifier-hidden-layer-sizes
  
  More Information about Random State
  https://stackoverflow.com/questions/42191717/python-random-state-in-splitting-dataset/42197534
  https://stats.stackexchange.com/questions/80407/am-i-creating-bias-by-using-the-same-random-seed-over-and-over
docstr_tok: ["Understanding","Architectures","in","MLP","Regressor","https",":","\/\/stackoverflow.com\/questions\/35363530\/python-scikit-learn-mlpclassifier-hidden-layer-sizes","More","Information","about","Random","State","https",":","\/\/stackoverflow.com\/questions\/42191717\/python-random-state-in-splitting-dataset\/42197534","https",":","\/\/stats.stackexchange.com\/questions\/80407\/am-i-creating-bias-by-using-the-same-random-seed-over-and-over"]
code: def __init__(self, **kwargs):
      """
      Understanding Architectures in MLP Regressor
      https://stackoverflow.com/questions/35363530/python-scikit-learn-mlpclassifier-hidden-layer-sizes
  
      More Information about Random State
      https://stackoverflow.com/questions/42191717/python-random-state-in-splitting-dataset/42197534
      https://stats.stackexchange.com/questions/80407/am-i-creating-bias-by-using-the-same-random-seed-over-and-over
      """
      super().__init__(**kwargs)
      self.arch = kwargs.pop(
          "arch", RandomNeuralNetwork.DEFAULT_ARCH_DICT
      )
      self.random_state = kwargs.pop(
          "random_state", RandomNeuralNetwork.RANDOM_STATE
      )
      self.nn = {}
      self.nn["confounder=>treatment"] = MLPRegressor(
          random_state=self.random_state,
          hidden_layer_sizes=self.arch["confounder=>treatment"],
      )
      self.nn["confounder=>outcome"] = MLPRegressor(
          random_state=self.random_state,
          hidden_layer_sizes=self.arch["confounder=>outcome"],
      )
      self.nn["effect_modifier=>outcome"] = MLPRegressor(
          random_state=self.random_state,
          hidden_layer_sizes=self.arch["effect_modifier=>outcome"],
      )
      self.nn["treatment=>outcome"] = MLPRegressor(
          random_state=self.random_state,
          hidden_layer_sizes=self.arch["treatment=>outcome"],
      )
  
file_path: dowhy/dowhy/utils/dgps/random_neural_network.py
imports: import numpy
  import pandas
  from sklearn.neural_network import MLPRegressor
  from dowhy.utils.dgp import DataGeneratingProcess
label_desc: Use image node_id 1 to create a new RandomNeuralNetwork object from inherited base classes: DataGeneratingProcess with example: obj = RandomNeuralNetwork()